Abstract
Enterprises of the transportation sector must face a huge competition. So, an efficient vehicles fleet management is crucial. The present work proposes a generic model adapted to the monitoring of a fleet of vehicles. This model is able to describe the information chain and the different decisional processes associated to the monitoring architectures. On a first “vehicle” level, each vehicle and also its context (cargo, user, environment and task) are considered. The vehicle composition is modelled according to a holonic hierarchy. On a second “fleet” level, data collected from all vehicles are analysed. The model is then applied to the monitoring of trucks tyres for a transport application of dangerous substances.
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References
Mbuli, J.W.: A multi-agent system for the reactive fleet maintenance support planning of a fleet of mobile cyber-physical systems: application to rail transport industry. Doctoral dissertation. Université Polytechnique Hauts-de-France (2019)
Trentesaux, D., Branger, G.: Data management architectures for the improvement of the availability and maintainability of a fleet of complex transportation systems: a state-of-the-art review. In: Service Orientation in Holonic and Multi-Agent Manufacturing, pp. 93–110. Springer, Cham (2018)
Pencolé, Y., Cordier, M.O.: A formal framework for the decentralised diagnosis of large scale discrete event systems and its application to telecommunication networks. Artif. Intell. 164(1–2), 121–170 (2005)
Bengtsson, M.: Condition Based Maintenance on Rail Vehicles-Possibilities for a more effective maintenance strategy (2003)
ISO 13374-1:2003 - Condition monitoring and diagnostics of machines - Data processing, communication and presentation - Part 1: General guidelines. https://www.iso.org/cms/render/live/en/sites/isoorg/contents/data/standard/02/18/21832.html
Alanen, J., Haataja, K., Laurila, O., Peltola, J., Aho, I.: Diagnostics of mobile work machines (2006)
Adoum, A.F.: An intelligent agent-based monitoring architecture to help the proactive maintenance of a fleet of mobile systems : application to the railway field, Doctoral dissertation. Université de Valenciennes et du Hainaut-Cambrésis (2019)
Chen, J., Lyu, Z., Liu, Y., Huang, J., Zhang, G., Wang, J., Chen, X.: A big data analysis and application platform for civil aircraft health management. In: 2016 IEEE Second International Conference on Multimedia Big Data (BigMM), pp. 404–409. IEEE (2016)
Jianjun, C., Peilin, Z., Guoquan, R., Jianping, F.: Decentralized and overall condition monitoring system for large-scale mobile and complex equipment. J. Syst. Eng. Electron. 18(4), 758–763 (2007)
Klas, G.: Edge computing and the role of cellular networks. Computer 50(10), 40–49 (2017)
Qiu, W., Kumar, R.: Decentralized failure diagnosis of discrete event systems. IEEE Trans. Syst. Man Cybern.-Part A: Syst. Hum. 36(2), 384–395 (2006)
Zhang, Q., Zhang, X.: Distributed sensor fault diagnosis in a class of interconnected nonlinear uncertain systems. Ann. Rev. Control 37(1), 170–179 (2013)
Le Mortellec, A., Clarhaut, J., Sallez, Y., Berger, T., Trentesaux, D.: Embedded holonic fault diagnosis of complex transportation systems. Eng. Appl. Artif. Intell. 26(1), 227–240 (2013)
Basselot, V., Berger, T., Sallez, Y.: Information chain modeling from product to stakeholder in the use phase - application to diagnoses in railway transportation. Manuf. Lett. 20, 22–26 (2019)
Sallez, Y., Berger, T., Deneux, D., Trentesaux, D.: The lifecycle of active and intelligent products: the augmentation concept. Int. J. Comput. Integr. Manuf. 23(10), 905–924 (2010)
Koestler, A.: The ghost in the machine (1967)
Ackoff, R.L.: From data to wisdom. J. Appl. Syst. Anal. 16(1), 3–9 (1989)
Rasmussen, J.: Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models. IEEE Trans. Syst. Man Cybern. 3, 257–266 (1983)
STMF. https://www.stmf.pro/
Mallouk, I., El Majd, B.A., Sallez, Y.: Optimization of the maintenance planning of a multi-component system. In: MATEC Web of Conferences, vol. 200, p. 00011. EDP Sciences (2018)
Egaji, O.A., Chakhar, S., Brown, D.: An innovative decision rule approach to tyre pressure monitoring. Expert Syst. Appl. 124, 252–270 (2019)
Domprobst, F.: Heavy truck vehicle dynamics model and impact of the tire. In HVTT14: 14th International Symposium on Heavy Vehicle Transport Technology, Rotorua, New Zealand (2016)
Damjanovic-Behrendt, V.: A digital twin-based privacy enhancement mechanism for the automotive industry. In: 2018 International Conference on Intelligent Systems, pp. 272–279. IEEE (2018)
Preethi, V., Sasi, R.S., Rohit, J.M.: Predictive analysis using big data analytics for sensors used in fleet truck monitoring. Int. J. Eng. Technol. 8(2), 6 (2016)
Prytz, R.: Machine learning methods for vehicle predictive maintenance using off-board and on-board data. Doctoral dissertation, Halmstad University Press (2014)
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Mallouk, I., Berger, T., El Majd, B.A., Sallez, Y. (2021). A Proposal to Model the Monitoring Architecture of a Complex Transportation System. In: Borangiu, T., Trentesaux, D., Leitão, P., Cardin, O., Lamouri, S. (eds) Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. SOHOMA 2020. Studies in Computational Intelligence, vol 952. Springer, Cham. https://doi.org/10.1007/978-3-030-69373-2_39
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